The rapid increase in demand for air transportation has resulted in a serious overload in the National Airspace System (NAS), impacting everyone involved. Passengers experience flight delays and cancellations more often, while airlines find themselves consuming more fuel as flights are placed in holding patterns more frequently. In addition, the problem is predicted to worsen. Studies show that within the next two decades, the demand for air transportation will double, if not triple. Unfortunately, the current capacity of the present airspace system cannot handle demand of that magnitude.
In the current system, the national airspace is divided into sectors, which are monitored by one or more air traffic controllers. Each sector has a certain capacity in terms of number of flights that can be handled in a given interval of time. Sector capacity is directly affected by a sector's complexity, which is the cumulative effect of all factors that influence an air traffic controller's ability to manage air traffic in a sector. Thus, an overloaded sector is the direct effect of a sector that is too complex. Accordingly, resolving the overloading of the sector involves decreasing the complexity.
In current day practice, the complexity of a sector is represented by a single variable, which is the number of aircraft in the sector. Currently, sectors are determined to be too complex (i.e., over capacity) if the aircraft count exceeds a predetermined threshold called the sector monitor alert parameter (MAP). When a sector is too complex, reducing the complexity warrants decreasing the aircraft count by rerouting or delaying the aircraft.
The notion of sector complexity is important even when part of a sector is unusable (due to convective weather or other operational reasons) and has been the focus of several studies, all of which have concluded that complexity involves more variables (metrics) than just the number of aircraft in a sector. To adequately represent such variables, more sophisticated methods of assessment are needed. Further studies have yielded linear regression models and neural networks for predicting sector complexity. Accordingly, these multivariable models can be utilized to reduce sector complexity without reducing the number of aircraft in the sector, thereby increasing sector capacity. However, despite the plethora of studies directed toward reducing the complexity of a sector, none of the studies have utilized the multivariable models.